Snow cover is often easily identifiable in visible-band satellite images because it typically possesses an albedo that exceeds almost all other land surface types. Snow may be identified manually by noting the magnitude of the reflectance or it may be identified automatically by the application of an algorithm that recognizes the specific spectral signature of snow. The spectral reflectivity of snow depends on a number of parameters including grain size and shape, impurity content, near-surface liquid water content, surface roughness, and solar elevation. The visible albedo is highly sensitive to the impurity content (Warren and Wiscombe, 1981). In the near-infrared region snow reflectance decreases strongly with wavelength and becomes primarily dependent on grain size (Grenfell and Perovich, 1981; Wiscombe and Warren, 1981). The presence of liquid water in a melt state has very little effect on the albedo. However, the larger grains which result from melt metamorphism result in a reduced near-infrared reflectance. At wavelengths above 1.4 the reflectance of snow amounts to only a few percent enabling good discrimination between snow and clouds since the reflectance of clouds remains high at that wavelength.
Some of the earliest applications of satellite remote sensing involved efforts to map and monitor the areal extent of snow cover. In fact, snow-cover extent is the longest available environmental product provided by satellite remote sensing. In 1966, the National Oceanographic and Atmospheric Administration (NOAA) began an operational program to map the Northern Hemisphere snow extent using available visible-band satellite data (Matson and Wiesnet, 1981; Matson et al., 1986; Robinson et al., 1993). Within the following ten years, researchers began to present results demonstrating the operational capabilities of satellite remote sensing in snow hydrology (Rango, 1975; Schneider et al., 1976).
During the past four decades much important information on continental to hemispheric scale snow extent has been provided by satellite remote sensing in the visible wavelengths. From 1966 to 1999 NOAA-NESDIS produced weekly snow extent charts for Northern Hemisphere land surfaces using visible-band satellite imagery (Robinson etal., 1993; Frei and Robinson, 1999). These NOAA charts were derived from the manual interpretation of Advanced Very High-Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellite (GOES), the European satellite (METEOSAT), Japan's geostationary meteorological satellites, and other visible satellite data by trained meteorologists. The charts were then digitized on a weekly basis using an 89 by 89 Northern Hemisphere polar stereographic grid with a nominal resolution of 190.5 km. The data values are binary and grid cells are classified as snow covered or snow free if the cell has more or less than 50% snow cover (Dewey and Heim, 1982).
In 1997, NOAA-NESDIS began the process of migrating to a more automated procedure for generating a daily, higher resolution (1024 by 1024 polar stereographic grid with a resolution of approximately 25 km) snow-cover analysis as the coarse resolution of the weekly charts had been shown to cause errors in the National Meteorological Center's Numerical Weather Prediction (NWP) models. The result was the Interactive Multisensor Snow and Ice Mapping System (IMS)
which incorporates a wide variety of satellite imagery (AVHRR, GOES, SSM/I) as well as derived mapped products (USAF Snow/Ice Analysis) and surface observations, and allows a trained meteorologist to produce a hemispheric analysis in one hour as opposed to 10 hours with the old weekly product (Ramsay, 1998). The new daily analysis and the old weekly product were overlapped for two winters to determine if the switch introduced any inhomogeneity into the existing weekly product (Robinson et al., 1999). The weekly product was phased out on June 1, 1999, but a "pseudo-weekly" map is generated by taking the Sunday IMS map and interpolating this back to the coarse resolution of the earlier weekly product.
The NOAA-NESDIS data set has been used extensively in analysis of snow-cover variability (e.g. Robinson and Dewey, 1990; Gutzler and Rosen, 1992; Groisman et al., 1994; Frei and Robinson, 1999), for investigating snow-cover linkages to atmospheric circulation and climate (e.g. Leathers and Robinson, 1993; Gutzler and Preston, 1997; Clark et al., 1999; Watanabe and Nitta, 1999), and for evaluating climate models (e.g. Foster et al., 1996: Frei et al., 2005). However, it is important to keep in mind that this product has well-documented limitations for regional-scale analysis of snow cover (Scialdone and Robock, 198755; Wiesnet et al., 1987; Robinson and Kukla, 1988; Wang et al, 2005). Sources of error include difficulties in the discrimination of clouds from snow, the use of previous estimates of snow cover in regions with persistent cloud cover, masking of snow on the ground by forests, and accurate charting in areas of patchy snow. To facilitate these types of analyses the National Snow and Ice Data Center (NSIDC), University of Colorado, has developed the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 3 (Armstrong and Brodzik, 2005), a Northern Hemisphere cryospheric product that combines snow cover and sea-ice extent at weekly intervals (Fig. 5.2). The snow data set is based on the weekly NOAA charts, revised by Robinson et al. (1993), for the period 1966-2005, while the sea-ice data set, based on passive microwave remote sensing, covers the period 1978-2005. This data set also includes monthly climatologies describing snow and sea-ice extent in terms of average conditions, probability of occurrence, and variance. The data set is produced in an azimuthal equal area projection (NSIDC Equal Area Scalable Earth Grid or EASE-Grid).
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